LeadDev LDX3 NYC

AI is in your engineering workflow. The token spend shows it, but the throughput doesn't. The human is still very much in the loop. Most software teams can't prove whether AI is actually helping, because the metrics they watch reward motion over delivery.

This is a context problem. The gains disappear between the feature branch and main because your agent doesn't fully understand how your system works. It pushes code that breaks in production, and you spend the next sprint dealing with the thrash instead of working the review backlog or shipping the next feature.

In this workshop, we build the plan to move your org from scattered adoption to an AI development lifecycle, the ADLC, where AI is a dependable part of how your team ships.

• First, we place your team on a map of eight AI context maturity levels, and you compare where you landed with the people around you. You'll see the three walls that stop teams at each level, and which one is in front of you.
• Second, you measure. We give you four delivery metrics that show where AI's gains leak out before production, and you run them to set a baseline for one team already leaning in. Real numbers, on your own work, so you can tell whether a team has actually moved up a level or just spent more tokens.
• Third, we map the path forward. We walk through what it takes to clear each bottleneck in order: good Q&A first, then stronger code review as more PRs go up, then reliable background agents for parallel work. You leave knowing which one to tackle next and how you'll measure it.

The thread through all of it: none of this works without a context layer underneath. An agent with access to every source still can't tell which one is right. That's what turns tokens spent into work shipped.

You leave with your team's place on the maturity model, a baseline you measured yourself, and a clear next step with the numbers to watch.

Your agents are fast, capable, and completely context-blind. They generate code that compiles but doesn't reflect how your system actually works. You're likely already seeing the impact: ballooning token costs, longer review cycles, and inconsistent outputs. More MCPs, rules, and bigger context windows give agents access to information, but not understanding. In this session, we dissect how teams pulling ahead use a context engine to give agents exactly what they need for the task at hand. Includes a short demo showing a real coding task with and without a context engine.